Database and Model for Dynamic scenario assessment V2

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Database and Model for Dynamic

scenario assessment V2

ADAI: Miguel Almeida, Luís Mário Ribeiro,

Domingos Viegas

AMRA: Alexander Garcia-Aristizabal, Giulio

Zuccaro, Maria Polese, Stefano Nardone, Marco

Marcolini

AEE: Marianne Grisel, Christophe Coulet

FMI: Karoliina Pilli-Sihvola

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The research leading to these results has received funding from the European Community's Seventh Framework Programme FP7/2007-2013 under grant agreement no. 284552 "CRISMA“

Deliverable No. D42.3

Subproject No. SP4 Subproject Title Models for

Multi-Sectorial Consequences

Workpackage No. 42 Work package Title Cascade Effects on

Crisis-Dependent Space-Time Scales

Authors ADAI: Miguel Almeida, Luís Mário Ribeiro,

Domingos Viegas

AMRA: Alexander Garcia-Aristizabal, Giulio Zuccaro, Maria Polese, Stefano Nardone, Marco Marcolini

AEE: Marianne Grisel, Christophe Coulet FMI: Karoliina Pilli-Sihvola

Status(F = Final; D = Draft) F

File Name CRISMA_D423_public

Dissemination level

(PU = Public; RE = Restricted; CO = Confidential)

PU

Contact [email protected]

[email protected]

Project www.crismaproject.eu

Keywords

Deliverable leader Name: Miguel Almeida, Domingos Viegas Partner: ADAI

Contact: [email protected];[email protected]

Contractual Delivery date to the EC

31.08.2014 Actual Delivery date to

the EC

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Disclaimer

The content of the publication herein is the sole responsibility of the publishers and it does not necessarily represent the views expressed by the European Commission or its services.

While the information contained in the documents is believed to be accurate, the

authors(s) or any other participant in the CRISMA consortium make no warranty of any kind with regard to this material including, but not limited to the implied warranties of merchantability and fitness for a particular purpose.

Neither the CRISMA Consortium nor any of its members, their officers, employees or agents shall be responsible or liable in negligence or otherwise howsoever in respect of any inaccuracy or omission herein.

Without derogating from the generality of the foregoing neither the CRISMA Consortium nor any of its members, their officers, employees or agents shall be liable for any direct or indirect or consequential loss or damage caused by or arising from any information advice or inaccuracy or omission herein.

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List of Figures

Figure 1: Business logic of the CRISMA framework. ... 2

Figure 2: Integration of the cascade effect model in the business logic of the CRISMA framework. ... 3

Figure 3: Diagram of cascade event chains identified for the occurrence of extreme weather conditions... 6

Figure 4: Diagram of cascade event chains identified for the occurrence of flood. ... 8

Figure 5: Diagram of the identified cascade event chains for Earthquake (from D42.2). ... 9

Figure 6: Photos of electric poles. ... 10

Figure 7: Images related to forest fires triggered by electric cable failure. a) Oliveira de Frades (Portugal) in 28/02/2004; b) Schematic view of top of pole 39; red circle indicates where the conductor failed (Plaintiff, 2013); c) Photograph of fire ignition area (Plaintiff, 2013). ... 10

Figure 8: Cascade events chain for pilot B. ... 14

Figure 9: Maximum water level in the area of Port-Neuf in La Rochelle during Xynthia. ... 14

Figure 10: Transition matrix developed for pilot B. ... 15

Figure 11: Workflow for the application of cascade effect model in Pilot D of CRISMA. ... 17

Figure 12: Logical flow of information and data for the assessment of the earthquake-earthquake scenario. ... 18

Figure 13: Building inventory in L’Aquila test case (Pilot D). The computation domain is divided in a regular grid, and for each grid element the total number of buildings and the proportion of different building classes are represented. ... 18

Figure 14: Simulated seismic sequence located at the NW of L’Aquila city, Italy. The main shock is a Mw 5.6 event. ... 19

Figure 15: Shake map of the triggering event, representing the spatial distribution of the Peak Ground Acceleration (PGA) in %g values... 20

Figure 16: Probability of having collapsed buildings in the target area after the occurrence of the main seismic event. ... 20

Figure 17: PGA values for different exceedance probability thresholds: (a) 1%; (b) 0.1%, and (c) 0.01%; and (d) resume of the result hazard curves. ... 21

Figure 18: Probability of having collapsed buildings in the target for the earthquake-earthquake scenario. The map represents the probability of collapsed buildings considering the potential triggered seismicity according with the characteristics of the seismic sequence triggered by the main seismic event. ... 22

Figure 19: Flow of information during the running of cascade effects for the selected scenario. “Application 1” is referred to the transition EQ-DEN and “Application 2” is referred to the transition DEN-FF. ... 23

Figure 20: Geographic distribution of the electric poles. ... 24

Figure 21: Map of the triggering earthquake intensity distribution with location of the electric power network (distribution lines – smaller poles). ... 24

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Figure 22: Fuel map for the region of L’Aquila. ... 25

Figure 23: Sketch of a T&D system for an EPN (TL = Transmission Lines, D = Distribution lines, TD [HV MV] = Transformation (from high to medium voltage) and Distribution station, TD [MV LV] = Transformation (from medium to low voltage) and Distribution station, L = Load) (adapted from D5.2 Deliverable of the EU project SYNER-G). ... 26

Figure 24: An example of tubular steel poles classification (from a commercial producer). ... 26

Figure 25: Definition of top displacement XMAX. ... 27

Figure 26 – Normalized pseudo-acceleration spectra... 27

Figure 27: Representation of the electric poles and the mass on the top... 28

Figure 28: IDA for the pole type 12B14. ... 28

Figure 29: Fragility curves of electric poles to a transversal force T1. ... 29

Figure 30: Sequence of images of a laboratorial test to determine the energy required for ignition by industrial electric discharge. ... 30

Figure 31: a) Plot of the Cumulative Distribution Function (CDF) of Log-normal, Gamma, Normal, Weibull, and Exponential competing models, and the empirical CDF of the observed data; b) CDF of the Log-normal (with parameters as those presented in Table 3), selected as the best model describing the observations. ... 31

Figure 32: Map for probability of cable failure. ... 32

Figure 33: Map for probability of fire ignition due to an electric cable failure. ... 33

Figure 34: Map for probability of fire ignition by electric cable failure due to earthquake. ... 34

Figure 35 – Map of the evolution of the forest fire (a) after the ignition by the electric discharge from a probable ignition point and other possible outputs from the FireStation software: b) rate of spread, c) linear intensity. ... 34

Figure 36: Stages of use of the cascade effect model. ... 35

Figure 37: General overview of the CE Map transformation mechanism. ... 36

Figure 38: WPS process in detail. ... 37

Figure 39: Diagram of cascade event chains identified for the occurrence of an earthquake... 40

Figure 40: Diagram of flood cascade event chain... 42

Figure 41: Diagram of forest fire cascade event chain... 44

Figure 42: Diagram of extreme weather cascade event chain. ... 45

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List of Tables

Table 1: Damage Probability Matrix for dike segment vulnerability. ... 13 Table 2: Transition matrix for pilot B ... 16 Table 3: Candidate distributions, PDF, estimated (MLE) model parameters and uncertainties, and Akaike Information Criteria (AIC). According to the AIC information, the model that best

describes the data is the Lognormal... 31 Table 4: Description of chain blocks identified for possible cascade event chains after an

earthquake. ... 41 Table 5: Description of chain blocks identified for possible cascade event chains after a flood. ... 43 Table 6: Description of chain blocks identified for possible cascade event chains after a forest fire... 44 Table 7: Description of chain blocks identified for possible cascade event chains for a case of extreme weather conditions. ... 46 Table 8: Description of blocks of cascade event chain identified for possible cascade event

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Glossary of terms

Term Definition

Domino effect "a cascade of events in which the consequences of a previous accident are increased by following one(s), as well spatially as temporally, leading to a major accident“ (Delvossalle, 1996) Cascade effect “the situation for which an adverse event triggers one or more

sequential events (synergetic event)” (Marzocchi et al. 2009) Serial domino

(cascade) effect

“Happening as a consequent link of the only accident chain caused by the preceding event” (Reniers, 2004)

Parallel domino (cascade) effect

“Happening as one of several simultaneous consequent links of accident chains caused by the preceding event” (Reniers, 2004) World state A particular status of the world, defined in the space of parameters

describing the situation in a crisis management simulation that represents a snapshot (situation) along the crisis evolvement. The change of world state, that may be triggered by simulation or manipulation activities by the CRISMA user, corresponds to a change of (part of) its data contents.

Multi-hazard To determine the probability of occurrence of different hazards either occurring at the same time or shortly following each other, because they are dependent from one another or because they are caused by the same triggering event or hazard, or merely threatening the same elements at risk without chronological coincidence.

Multi-risk To determine the whole risk from several hazards, taking into account possible hazards and vulnerability interactions (a multi-risk approach entails a multi-hazard and multi-vulnerability perspective). Damage Definition 1 (context of socio-economic vulnerability, related with

concept of impact): the amount of destruction or losses, either in health, financial, environmental functional and/or other terms as a consequence of an occurred hazard (Marzocchi et al. 2009, 2012)

Definition 2 (context of structural damages): physical harm that impairs the value, usefulness, or normal function of something (Oxford Dictionaries,

http://oxforddictionaries.com/definition/english/damage) Adverse event Anything produced by a risk source in a certain area that can

generate phenomena with potentially adverse consequences. The adverse event can be due to a risk source located inside or outside the site where the event takes place (Marzocchi et al. 2009).

Risk source Element which alone or in combination has the intrinsic potential to give rise to risk

(ISO/Guide 73:2009(en) Risk management — Vocabulary

https://www.iso.org/obp/ui/#iso:std:iso:guide:73:ed-1:v1:en)

Event Occurrence or change of a particular set of circumstances. An event can be one or more occurrences, and can have several causes. An event can sometimes be referred to as an “incident” or “accident”. (ISO/Guide 73:2009(en) Risk management — Vocabulary

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ACRONYMS

Term Definition

CE Cascade effect(s)

CF Cable failure

DEN Damage to electricity network

E Energy required to have ignition

EQ Earthquake

FF Forest Fire

IDA Incremental Dynamic Analysis

k Poles stiffness

OOI Object of interest

P Power of electric discharge

TPD Transition Probability Data

PGA Pick ground acceleration

SDOF Single degree of freedom

T Elastic period of poles

t Time to ignition

XMAX Top displacement of electricity poles

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Executive Summary

This deliverable presents a concept model to assess the eventual occurrence of cascade effects that was produced under the CRISMA Project. This deliverable is a consequence of other deliverables produced in the CRISMA Project, namely D42.1 (Garcia-Aristizabal et al., 2013) and D42.2 (Almeida et al., 2013). In this deliverable, besides an integrative compiling of the results previously achieved, a description of the final concept model is carried out. Moreover, an application of the concept model in a scenario where a forest fire was triggered by an earthquake is detailed. The developed cascade effect model is available in the CRISMA catalogue (https://crisma-cat.ait.ac.at/).

First chapter of D42.1 introduces the theoretical concepts of cascade effects and describes the concept model dynamic scenario assessment due to cascade events and its inclusion in the general CRISMA tool.

Cascade effects will have two applications in the pilots of CRISMA project, namely in Pilot B, dealing with coastal submersion hazards, and Pilot D, relating an earthquake event to a forest fire. Additionally an application with no further achievements was also planned for Pilot A concerning to extreme weather conditions event. The scenarios for quantitative analysis are described in Chapter 2 of this deliverable. In Chapter 3, the quantitative analysis and the results achieved by application of the cascade effects conceptual model are detailed.

The final conclusions and statements on the database and model for dynamic scenario assessment integrating cascade events in a multi-risk assessment scheme are described in Chapter 5.

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1. Introduction

A hazard crisis situation may be due to the occurrence of a single hazard event with large impacts or due to several hazard events that occur simultaneously. Hazard events occurring at the same time may have independent causes or may result from a sequence of triggering hazard events. The outcome of a situation for which an adverse event triggers one or more sequential events (synergetic event) is called “cascading effects” (Marzocchi et al., 2009, 2012).

The perception and understanding of the potential occurrence of cascading effects is of great relevance for planning and response activities since a surprising situation in a hazard crisis scenario may endanger people and goods, and may nullify a strategy that was developed accounting for a scenario in which the triggering event was a single occurrence.

1.1. Multi-hazards analysis

The development of a model to manage with cascade effects has several challenges as several hazards are approached in a single application. A detailed description on existing multi hazards and multi risks assessment methods was carried out in D42.1.

In the scope of the CRISMA Project, “Multi-hazard analysis” is seen as the determination of the probability of occurrence of different hazards either occurring at the same time or shortly following each other, because they are dependent or because they are caused by the same triggering event or hazard, or merely threatening the same elements at risk without chronological coincidence. By this definition, multi-hazard can be applied in different perspectives: (1) multi-hazard seen as the assessment of different independent hazards that threaten a common area or common exposed elements; (2) multi-hazard seen as the assessment of triggering, domino, or cascade effects and (3) multi-hazard seen as the assessment of possible hazard interactions (at vulnerability level) (Garcia-Aristizabal and Marzocchi, 2012).

In the development of a cascade effect model, the perspective of multi-hazard seen as the assessment of triggering, domino, or cascade effects is followed. To assess the likelihood of cascade effect occurrence, a transition between two related hazard events must be considered. The main goal of the cascade effect model is to provide information about the occurrence probability of a series of events. Therefore, two main aspects must be considered: 1) the possible cascade event chains resulting from a triggering hazard event, and 2) the transition probabilities from the triggering hazard event to the triggered events.

1.2. Cascade Event Chains Database

Based on a methodology proposed by Garcia-Aristizabal et al. (2013), a database with many identified possible cascade event chains was created for the hazards managed in the CRISMA project, namely: flood, earthquake, forest fire, release of chemical substances and extreme weather conditions. This methodology and the preselected scenarios are described in detail in D42.2 (Almeidaet al., 2013).

The event chain scenarios identified are presented in APPENDIX (1)

Description of chain blocks identified for possible cascade event

chains

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. The sequence of events is often cyclic as a certain event type may occur in the same chain more than once. This repetition may be direct (e.g. earthquake earthquake) or indirect (e.g. ex: flood damage to structural protection flood). When a potential repetition is verified, the symbol … was used to indicate the cyclic chain. In order to simplify the event chain scenarios diagram, shortenings were created.

The possibility to have certain scenarios strongly depends on the specific case under analysis. For example, the possibility of having an explosion triggered by damages to an industrial facility depends on the type of industrial facility damaged in the area of interest. Under this perspective, the proposed diagrams must be adapted to the characteristics of the hazard event and to the area of interest.

The event chains database is of great importance as it allows the user to choose the chain of interest and the cascade event analysis that shall be performed.

1.3. Integration of cascade effects into the general framework of

CRISMA

Figure 1 presents a scheme of the CRISMA framework. There is an initial world state that is changed by the specific hazard simulation models originating a new world state. Each model requires different inputs related to the hazard (e.g., earthquake intensity) or related to the exposed elements (e.g., fuel cover distribution). The CRISMA tool also allows the user to play with other models associated to the mitigation options, the resources management and/or the choice of output. Finally, each simulated world state resulting in a set of impacts which are traduced as indicators, criteria and costs that support the user decision making.

Figure 1: Business logic of the CRISMA framework.

The integration of the cascade effect model in the CRISMA framework relates two consecutive world states combining two different hazard events. Figure 2 shows a diagram that intends to explain the integration of the cascade effect tool in the CRISMA framework. Facing a given world state (WS0), the user may access the probabilities of occurrence of a hypothetical hazard event triggered by the hazard event of WS0. The cascade effect model is so appealed and the user is invited to choose the cascade event chain of interest to

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perform calculations. This event chain may have one or more transitions between hazard events. Besides the specific information of WS0 that is required as input for the cascade effect model, the transition probability data between the two hazard events is also necessary.

Figure 2: Integration of the cascade effect model in the business logic of the CRISMA framework.

The cascade effect model provides information about the probability of occurrence of a cascade of hazard events originated by the triggering hazard event. This information is usually spatial as different probabilities occur for different locations. Since the information about the probability spatial distribution of the eventual occurrence of a triggered event is available, the user may simulate a new scenario to assess the impacts of a cascade effect occurrence simulating and creating a new world state.

The interaction between the CRISMA framework and the cascading effect model (CEM) is guaranteed by the “Cascade Events Configuration and Interaction View” building block, hereafter shortened to “cascade effect view (CEV), which is available on the CRISMA Catalogue ( https://crisma-cat.ait.ac.at/bb/Cascade-Events-Configuration-and-Interaction-View). The Cascade Effects View is a user interaction building that allows a user to configure and run a Cascade Effects Scenario. The user can select a triggering event (for example, an earthquake) and provide may either specify the characterization of the event (Simulation Control Parameter) and thus initiate a new Simulation Model Run for this particular event, or select (if available) the output of a past event or an event already simulated. When the triggering event has been selected and characterized, the CEV shows the possible paths of event chains that are available. The user may select one of the paths and the Cascade Effects View will highlight eventual secondary events triggered after the previous one. In this way, the user may select a specific chain of events that are interested to assess or to interrupt a chain if he decides to stop the analysis in an intermediate point. For each of the events, the user may either characterize the event by providing the respective simulation control parameters or select the output of a past event. In parallel, the user may access to the available transition probability data (TPD) using this interactive building block. After selecting the event chain, the list of the available related transition probability data are shown for each transition. The user shall select the data to be used for each transition. The interaction view allows the creation of a new TPD and the editing of the existing TPD.

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The cascade effect model will use several inputs provided by the cascade effects view in order to produce the probability map(s) that will be shown by the same interactive building block. The CEM will not interact with the user as this will be carried out by the CEV. CEM is a black box that will generate cascade effects probability maps that will be show to the user by the CEV.

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2. Scenarios for quantitative analysis

In D42.2, possible adverse event chains were identified and described for five different triggered hazards, namely: earthquakes, floods, forest fires, extreme weather conditions and release of chemical substances. Only three of these hazards were planned to use the cascade effect conceptual model in CRISMA Pilots. The scenarios will be detailed in the following paragraphs. In all, the triggering event shall be seen as an already occurred event (pre-determined occurrence) and the consequent events shall be seen as episodes which the end-user intends to evaluate the probability of happening and the possible impacts associated.

2.1. Triggering hazard: Extreme Weather Condition (Pilot A)

Extreme weather conditions can cause various cascade events chains. In Pilot A, the original purpose was to assess the probability of a crisis scenario taking place in northern Finland due to extreme winter weather. The scenario is initiated when a low pressure system forms in southern Scandinavia in mid-December. The system moves towards Finland, bringing lots of snow (30 cm/day over land) which, together with freezing drizzle causes very poor road conditions. Crown snow-load starts to accumulate on roads, trees and power lines. A second low pressure system with snow storms and winds gusting up to 20 m/s on land arrives one day after the first one. Two days later, a third low pressure system hits with extremely heavy winds (gusts on land 30–40 m/s), causing major problems on road, electricity and communication networks. Finally, the low pressure centre moves slightly southeast and cold air starts to flow from the northwest. Temperatures fall widely below -10°C. During the following 2-3 weeks, the cold weather spreads into the area and daytime temperature falls widely down to -20–45°C, causing the need for evacuation. The return period for this kind of event has not been estimated, but based on expert judgement it is once in several hundred years.

The main focus of Pilot A is the event chain from the initial weather event to the damage to the electricity power lines, which, combined with the cold spell, causes health problems to vulnerable communities, such as the elderly, causing a need for evacuation. Two chains of events cause damage to the power lines: the falling snow and heavy winds damage either the power lines directly or the damage is caused by leaning or falling trees (see Figure 3). The initial weather event is pre-determined; therefore the probability of the event is assumed to be one. However, the extent of the damage is not known and it depends on several factors, such as wind direction, forest cover, soil etc.

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Figure 3: Diagram of cascade event chains identified for the occurrence of extreme weather conditions.

As this kind of an event has never happened before in northern Finland, no historical data could be used to assess the transition probability data for the cascading event. Therefore, a method called expert elicitation was used to obtain subjective probabilities of the event chain. The study was conducted at the Pilot A demo seminar in Kemi, Finland on the 7-9th of April, 2014. The experts used in the elicitation were emergency service actors and representatives of the relevant municipalities and electricity distribution companies, who participated in the event.

The result of the elicitation was that the probability of the original scenario of pilot A is practically zero due to three reasons: 1) over 70% of the electricity distribution cables in Kemi area are underground cables; therefore, storms cannot damage them; 2) for overhead cables, trees so high up north are too short and weak to cause any major damage; and 3) the damage caused by snow and other perils has been historically repaired in a short period of time (the majority in 30 minutes, all in less than two days). The outcome of the expert elicitation resulted in a modification of the scenario description in Pilot A. The new description takes a long power blackout as given and assumes direct impacts of occurring because of that. Therefore, no cascade event scenario can be described or assessed. Due to the abovementioned reasons, it was decided to not use

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cascade effect model in Pilot A. The main decision was the exclusion of these event chains from the general diagram. Therefore, hereinafter, this use case for cascade effect will be not developed.

2.2. Triggering hazard: Coastal Submersion (Pilot B)

In these particular applications the triggering flood is referred to a costal submersion based on the Xynthia coastal floods occurred in France in 2010. Two of several event chains (Figure 4) having a flood as the triggering event are planned to test in the Pilot B of CRISMA Project.

The first event chain is: Flood Damage to Structural Protection Flood. The violence of the tides may damage or destroy the dams and dikes which are protecting the land area. This scenario of destruction exposes the terrestrial elements to water, even for lower water levels that would be not so relevant if the protection barriers were intact.

The second event chain is: Flood Damage to Industrial Facility Release of chemical substance. The flooding waters may damage the electricity network, leading to a power cut and causing a dysfunction of a wastewater treatment plant. The wastewater that cannot be treated nor stored is so discharged in the ocean affecting its environmental quality and possible triggering to eutrophication problems.

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Figure 4: Diagram of cascade event chains identified for the occurrence of flood.

2.3. Triggering hazard: Earthquake (Pilot D)

Figure 5 lists the several identified event chains that can be initially triggered by an earthquake. In Pilot D, two applications of cascade effects will be demonstrated: (1) earthquake earthquake; and (2) earthquake damage to electricity network forest fire.

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Figure 5: Diagram of the identified cascade event chains for Earthquake (from D42.2).

In the first scenario, the case of a seismic sequence triggered by a main shock is analysed. This case is based on the fact that the occurrence of a medium-to-big earthquake has the capacity to produce a perturbation of the stress field around the source of the main shock, stimulating the occurrence of triggered seismicity and, therefore, increasing the seismic hazard in the short-term. Accounting for the characteristics of the triggered seismicity, the objective of this scenario is to assess the effects of the generated seismic sequence in the short-term seismic hazard, and to calculate the possible damages associated with the triggered seismicity. : this scenario both the updated building inventory (after the occurrence of the main shock) and time-dependent vulnerabilities are considered.

The second scenario links an earthquake to a forest fire. In this scenario, a Mw 5.6 earthquake is the triggering event and the end-user plays with CE (cascade effect) model to evaluate the possibility of having a triggered forest fire, initiated by a cable failure in the

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electric network. In this scenario, the earthquake provokes damages to the electricity network specifically in the cables joints/couplings device (Figure 6a) near to the pole causing a rupture in the electric cable. As this electric cable is energized, it ionizes the air to the ground and consequently an electric discharge happens. In Figure 6b shows an example of air ionization caused by the proximity of a tree branch and Figure 6c shows a fire ignition caused by an electric discharged. Both examples follow the same principle of the application managed in Pilot D however in the CRISMA application the area reached by the electric arc is covered by surface fine forest fuels possible triggering an ignition which may develops to a forest fire.

(a) (b) (c)

Figure 6: Photos of electric poles.

There are several forest fire events triggered by an electric cable failure. Figure 7a shows an image of a cable damaged which was sufficient to create an electric arc to the ground originating a large forest fire. The most important forest fire event triggered by an electric failure occurred in Victoria (Australia) in February, 8th 2009 (the Kilmore Fire). This fire event was originated by an electric cable failure of the pole 39 (Figure 7b) driving to the fall of the cable in a wild land area (Figure 7c). The safety system interrupts the energy supply in 0.25s and three attempts to turn on the power for a total time of 4.48s were registered. Among several impacts, this fire results in 119 people dead. The court decision condemned the responsible to pay about 500 million Australian dollars as compensation for the losses.

(a) (b) (c)

Figure 7: Images related to forest fires triggered by electric cable failure. a) Oliveira de Frades (Portugal) in 28/02/2004; b) Schematic view of top of pole 39; red circle indicates where the

conductor failed (Plaintiff, 2013); c) Photograph of fire ignition area (Plaintiff, 2013).

A scenario of a forest fire following an earthquake assumes a great interest in the operational point of view. On one side the earthquake event requires the deployment of all

Cable damaged over the ignition point

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the available civil protection means, including firefighters. If a forest fire stars, some of these units must be allocated to fight the fire as soon as possible to avoid the enlargement of the fire and the appearance of a new hazard event requiring a major concern. On the other side, if the fire spreads to threat a village, people cannot stay inside houses because of the earthquake impacts and of eventual replicas, but cannot stay outside if the village become immersed in smoke. In this scenario, the evacuation may be the only option which must be planned in advance because the evacuation routes may also be disrupted by the earthquake and by the fire. Therefore, a preventive evaluation of this chain of events is of great interest and shall be object of reflection. The cascade effect tool would help in this reflection and would support the decision making.

Running of cascade effects model in this scenario will provide the spatial distribution of the probability of having a fire ignition in a fuel bed caused by an electric cable failure triggered by an earthquake. With this information, the end user may use a forest fire behaviour prediction model (as for example FireStation, which has been included in the CRISMA Catalogue) in order to assess the possible impacts. This information may support planning in a short or long term.

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3. Quantitative analysis and results

The scenarios for application of the tool for assessing cascade effects were previously described. Once the event chains of interest are selected, a number of input data is required as for example information related to the world state and information about the probabilities for the occurrence of transition between events.

Considering a specific scenario, after the occurrence of the triggering event the initial world state changes as a consequence of the impacts resulting from that hazard event. However some elements may be time dependent vulnerable and therefore the word state may be continuously changing after the triggering event. Time dependent vulnerability is presented in in deliverables D43.1 and D43.2, where much information on this subject is available. Nevertheless to access the cascade effects tool, the world state in the moment of the plausible transition must be defined.

The transition probability data (TPD) are essential to determine the likelihood of the transition. TPD can be previously uploaded to the system in the platform database by the administrator or may be provided or changed by the user during the utilization of the cascade effect model. The inputs must be available as geo referenced data or text data allowing calculations with geo reference data.

3.1. Triggering hazard: Coastal Submersion (Pilot B)

3.1.1. Flood Flood

3.1.1.1. General perspective

In case of coastal submersion, several phenomena can damage dikes such as external erosion, internal erosion and overflow.

Three damage levels are considered for the exposition of dikes towards storm surges or floods. First, the dike resists to the hazard and thus there is “no failure”. Second, the dike starts to fail that means a “breach” is formed. Within CRISMA, in the scope of the simulation of dike breaches (with TELEMAC-2D), we will consider that the breach formed within the segment will be set at 10% of the segment’s length that means at a maximum of 25 meters. Third, it is the “total failure”, the dike totally collapsed.

When the dyke is damaged, it loses its capacity to protect population, buildings, networks, etc. behind it. If a second coastal submersion occurred before the reparation of the dykes the flood extension can be much more important than in the first case.

3.1.1.2. Input Data

Two input data are required:

1. The localisation and the initial status of the dike

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3.1.1.3. Transition Probabilities

According to the physical status of the dike, the consequences of an overflow above the dike will be different. It is possible to express these different responses to the hazard intensity (water level overflowing above the dike segment) in a table (see Table 1), where the probabilities of attaining the different damage levels are synthesized.

The result for each segment of dike is a probability to resist, breach or fail. The end user will be able to see on a map the segments of dike with a colour:

Green if the maximum of chance is to resist Orange if the maximum of chance is to breach Red if the maximum of chance is to totally fail

Table 1: Damage Probability Matrix for dike segment vulnerability. Water level

above the dike

Status

Good Medium Poor

<20 cm 99,9% No failure 0,1% Breach 0% Total Failure 99% No failure 1% Breach 0% Total Failure 10% No failure 80% Breach 10% Total Failure 20 to 50 cm 99% No failure 1% Breach 0% Total Failure 10% No failure 80% Breach 10% Total Failure 5% No failure 15% Breach 80% Total Failure >50 cm 98% No failure 2% Breach 0% Total Failure 5% No failure 15% Breach 80% Total Failure 0,1% No failure 4,9% Breach 95% Total Failure >1 m 10% No failure 80% Breach 10% Total Failure 0,1% No failure 4,9% Breach 95% Total Failure 0,0% No failure 0,1% Breach 99,9% Total Failure

On the pilot B application, the user have to decide the behaviour of each dike segment according the classification and of course its own expertise. If this expertise is not available, it’s possible to automatically fix the behaviour of the dike segment according to the vulnerability classification.

If after an event (or a simulation of the event), the dike segment vulnerability is classified: with an “orange colour” meaning that it is likely to breach or if the user decide

this segment to breach, then, it will be reclassified in the poor status.

with a “red colour” meaning that it is likely to totally fail or if the user decide this segment to fail, then, it will be reclassified in the “collapsed” status

After the original event, we are able to modify the classification of the dykes in the World State and a new simulation can be done for another flood.

3.1.1.4. Output results

As previously mentioned, each dike segment will be classified according to the vulnerability and the probability to fail. If the user assumes the failure of one dike segment, a new World State (concerning the dykes) and a new flood extension will be created by the specific software provided by the general CRISMA tool.

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3.1.2. Flood Damage to Electricity Network Release of Chemical Substance

The first event is a coastal submersion. The impacts of the submersion include damages to the electricity network. These damages trigger a power cut and immediately the total dysfunction of a wastewater treatment plant. The wastewater then cannot be treated and is directly discharged in the ocean which pollutes the environment (ocean and submerged areas) (Figure 8).

Figure 8: Cascade events chain for pilot B.

The aim is to describe the cascade chain event with a transition matrix of probabilities. Within the pilot B, we use the historical event of Xynthia storm surge that occurred in February 2010 and led to coastal submersions. During Xynthia, in the municipality of La Rochelle, several areas were submerged. More particularly, the area of Port-Neuf where the wastewater treatment plant is located was flooded (Figure 9). Due to the water, the electricity network was damaged in this area. This triggered the dysfunction for several weeks of the wastewater treatment plant of Port-Neuf. The wastewater directly flowed in the natural environment (release of chemical substance).

Figure 9: Maximum water level in the area of Port-Neuf in La Rochelle during Xynthia.

The power cut leading to the plant’s dysfunction has two possible sources. First, the submersion reaches the nearest electrical converter leading to a power cut. Second, the submersion reaches the latest pumping station where the effluents are regulated following

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a short circuit due to water entering in contact with electrical components. In the two cases, a bypass of the effluents would be immediately created and the natural environment would be polluted by the discharge of wastewater. The daily volume of wastewater discharged as well as the concentration of polluting elements can be calculated considering the plant capacity.

Moreover, it can be noted that the plant requests power to function. In other words, a power cut leads irremediably to a total dysfunction of the plant and a bypass of the wastewater. Then, we can state that the probability that a power cut trigger to pollution by wastewater bypass is 100%.

3.1.2.2. Input Data

There are 3 input data:

1. The localisation of the wastewater plant

2. The characteristic of the original flood on the localisation of the wastewater plant 3. The characteristic of the daily volume of wastewater discharged. The discharge

of the wastewater as well as the concentration of the pollutant can be calculated with the capacity of the wastewater treatment plant. The Port-Neuf plant capacity is 170 000 population equivalents. Then, the pollution is characterised by:

Wastewater discharge: 25 500 m3/day

Concentration in biochemical oxygen demand (BOD5): 2.35x10-3 mg/L Concentration in chemical oxygen demand (COD): 5.29x10-3 mg/L Concentration in phosphorus: 1.57x10-4 mg/L

Concentration in Total Kjeldahl Nitrogen (TKN): 5.88x10-4 mg/L 3.1.2.3. Transition Probabilities

The transition matrix is developed to express the probability that a coastal submersion triggers pollution by wastewater in the ocean. The intensity of coastal submersion is expressed with the water level above ground level. The intensity of pollution is expressed by the wastewater discharge and the concentration of the released pollutant substances within the wastewater. These are unique values depending on the plant capacity. Indeed, the wastewater release in the natural environment due to the power cut is the amount of waste water arriving in the plant. Then, we will assess the probability to have pollution from wastewater for different coastal submersion intensity. Actually, the matrix is a vector (Figure 10).

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The probability P(Poll|Sub) within the transition matrix to assess is the combination of two probabilities:

The probability P(Power cut|Sub) that the coastal submersion leads to a power cut in the plant area.

The probability P(Poll|Power cut) that the power cut lead to the total dysfunction of the plant and the waste water discharge in the ocean.

We stated above that the probability P(Poll|Power cut) that the power cut lead to the total dysfunction of the plant is 100%. Then by combining the two probabilities, the probability P(Poll|Sub) that the coastal submersion triggers the pollution is the probability P(Power cut|Sub) that the coastal submersion triggers a power cut. (Equations 1, 2 and 3)

P(Poll|Sub) = P(Power cut|Sub) X P(Poll|Power cut) [1]

= P(Power cut|Sub) X 1 [2]

= P(Power cut|Sub) [3]

It can be noted that in case of storm surge, the power is not put back to service before 48 hours. In the case of pilot B, we only model 48 hours and thus the power will not be restored at the end of the simulation.

In the municipality of La Rochelle, the electrical converters are not in the flooded areas. Therefore, only the power cut by the submersion of the pumping station will be considered. We suppose that the electrical components of the latest pumping station before the plant is at 20 cm above the ground level. Then, as soon as the water level reaches these 20 cm, the probability to have a short circuit is 100%. If the water level is lower than 20 cm, the power cut is possible due to humidity or due to the salinity. Considering different classes of submersion intensity (water level above ground level), it is possible to assess the probabilities to have a power cut. (Table 2)

Table 2: Transition matrix for pilot B

Water level 0–5 cm 5–10 cm 10–15 cm 15–20 cm >20 cm

Probability to have a

power cut 0.01 0.1 0.15 0.2 1

As it was noticed before, the probability that the submersion triggers a pollution by wastewater spill is the same that the probability that the submersion triggers a power cut. 3.1.2.4. Output results

Results come from a new simulation of the coastal event with pollutant transport. It hasn’t be done yet, in reason of the too long computation duration of TELEMAC model. Output results will be shown in Deliverable D53.1 – Demonstrator of Pilot B – to be delivered in month 38th.

3.2. Triggering hazard: Earthquake (Pilot D)

As it was previously mentioned, the cascade effect application having an earthquake as the triggering hazard deals with two different event chains: (1) earthquake earthquake;

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and (2) earthquake damage to electricity network forest fire (fire ignition). Figure 11 shows the workflow for the use of cascade effect in Pilot D of CRISMA having an earthquake as the initial triggering event. This diagram shows the several inputs, the functions and the world states required for the two scenarios used for demonstration.

Figure 11: Workflow for the application of cascade effect model in Pilot D of CRISMA.

3.2.1. Earthquake Earthquake

As can be seen in Figure 11, the earthquake-earthquake scenario consists on the assessment of the potential impacts of the triggered seismicity that characteristically happens after a seismic event of certain magnitude. Forecasting the future behaviour of seismic sequences is not an easy task, and currently is a subject of intense research on applied seismology. Assuming that a given model can be used to forecast in the short-term the likely seismicity rates (e.g. in short-terms of number of events/day) and its expected spatial distribution, then short-term seismic hazard assessment can be performed and the expected damages caused by the triggered seismicity can be continuously updated. A logical representation of the data requirements and interactions is represented in Figure 12.

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Figure 12: Logical flow of information and data for the assessment of the earthquake-earthquake scenario.

3.2.1.2. Input Data

Different kinds of input data should be available to assess this scenario. First, an initial building inventory in the target area needs to be created. In this example, the initial building inventory is represented in Figure 13. The target area is divided in a regular grid, and the building inventory is represented, for each grid element, as the number of buildings within each cell and the proportion of different building classes. Each building class corresponds with a specific set of fragility functions.

Figure 13: Building inventory in L’Aquila test case (Pilot D). The computation domain is divided in a regular grid, and for each grid element the total number of buildings and the proportion of different

building classes are represented.

Other input data necessary to build this example is (1) an earthquake acting as the triggering event, and a seismic sequence occurring as a consequence of the initial triggering event. In this example, we simulate the occurrence of a main shock – aftershock sequence occurring in a zone located at the NW of L’Aquila city, as shown in Figure 14.

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The characteristics of the seismic sequence are simulated assuming seismicity rates, size-frequency distribution and spatial distribution of the events of similar past sequences in this region. In a near real-time application, these parameters can be fixed assessing using the occurring seismic sequence.

Figure 14: Simulated seismic sequence located at the NW of L’Aquila city, Italy. The main shock is a Mw 5.6 event.

The shake map of the triggering event is plotted in Figure 15 and represents the spatial distribution of the Peak Ground Acceleration (PGA) in %g units. These intensity values, together with the fragility functions for the different building classes, can be used to calculate the probability of having building damages (e.g. collapse) in the target area. The results of the damage probabilities for the main shock are represented in Figure 16.

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Figure 15: Shake map of the triggering event, representing the spatial distribution of the Peak Ground Acceleration (PGA) in %g values.

Figure 16: Probability of having collapsed buildings in the target area after the occurrence of the main seismic event.

3.2.1.3. Transition Probabilities

Using the triggered seismicity, short-term seismic hazard assessment can be performed using the data (i.e. spatial location, magnitude, shake maps) of the forecasted seismicity. Figure 17 shows examples of the spatial distribution of PGA values for different exceedance probability thresholds: 1% (Figure 17a), 0.1% (Figure 17b), and 0.01% (Figure 17c). The resulting transition probabilities for this scenario are the exceedance probabilities associated with different PGA values. At each grid element of the calculation domain, these transition probabilities can be represented as a hazard curve. A summary of the resulting hazard curves for the whole domain are represented in Figure 17d.

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a) b)

c) d)

Figure 17: PGA values for different exceedance probability thresholds: (a) 1%; (b) 0.1%, and (c) 0.01%; and (d) resume of the result hazard curves.

Using the updated building inventory (after assessing the direct impact of the triggering earthquake) and the transition probabilities shown in Figure 17, it is possible to calculate the expected impact of the likely triggered seismic sequence. Figure 18 shows the probability of having collapsed buildings in the target area for the earthquake-earthquake scenario, according with the characteristics of the seismic sequence triggered by the main seismic event. A direct comparison with the direct impacts expected after the main event (e.g. between Figure 16 and Figure 18 may be performed in order to assess the expected effects of the triggered seismicity.

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Figure 18: Probability of having collapsed buildings in the target for the earthquake-earthquake scenario. The map represents the probability of collapsed buildings considering the potential triggered seismicity according with the characteristics of the seismic sequence triggered by the main

seismic event.

3.2.2. Earthquake Damage to Electricity Network (Cable failure) Forest Fire

Figure 19 shows the structure of circulation of information in the application of cascade effects to this scenario. Besides the probability function regarding to the fragility curve of the electric system and to the probability of having ignition after the electric cable failure, three categories of inputs must be previously available on the world state, namely: the location of the electricity network, the intensity distribution of the earthquake and the fuel cover in the area of interest.

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Figure 19: Flow of information during the running of cascade effects for the selected scenario. “Application 1” is referred to the transition EQ-DEN and “Application 2” is referred to the transition

DEN-FF.

The probability of having a cable failure in the electric system (Application 1 in Figure 19) and the consequent formation of an electric arc results from the intensity distribution of the earthquake and the fragility curve of the electric devices. The intensity of the earthquake along the electric network is a part of the world state and information is available on the shake map. The fragility curve relates the intensity of the earthquake to the potential damage caused in the electric network along the line.

The probability of having an ignition started by an electric discharge (Application 2 in Figure 19) results from the fuel classification of the area where the electricity cable failure is located and the probability model of ignition by electric discharge. The fuel classification is a part of the world state and is provided by the fuel map of the area. If the electric discharge occurs in a non-fuel area, as for example a road, the probability of ignition is null. However, if the electric discharge occurs in a fuel area, as for example a grass land, the probability of ignition shall be determined by the use of the probability ignition model that will be detailed later.

If we attend to the whole event chain EQ-DEN-FF, the probability of having a fire ignition due to an earthquake is given by the product of the probability of having an electric cable failure and the probability of having a fire ignition triggered by an electric cable failure. 3.2.2.2. Input data from the wold state

Since the cascade effect model is appealed and the user selects the event chain linking the earthquake to damage to electricity network (cable failure) and to forest fire, some information regarding to the world state is required. That information is following detailed. Location of the electric network

The location of the electric network used for this example is not real since it was defined in the most opportune locations. For convenience of Pilot D, the electric line was designed to pass close to the Village of Castel del Monte in order to have a forest fire threating this community. On the other hand, this line was designed to cross the area of interest to have

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as many probabilities calculations as possible. Apart those two assumptions, the poles are randomly located only taking precautions to do not put a pole in an unlikely place such as in the middle of a river. The separation between two successive poles is around 300m.

Figure 20: Geographic distribution of the electric poles.

Map of EQ intensity distribution

The occurrence of a Mw5.6 earthquake in the NE part of the domain has been simulated as the triggering event for this scenario. The shake map of this event, represented by the intensity of the ground motion in the area of interest, is shown in Figure 21. Using the shake map of this event, the peak ground acceleration (PGA, in %g) of the ground motion is calculated at the base of each of the poles of the electric network considered for this example.

Figure 21: Map of the triggering earthquake intensity distribution with location of the electric power network (distribution lines – smaller poles).

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Fuel map

The fuel map is important to evaluate the probability of ignition as it has the information of the class of fuel where each electricity pole is located.

To be harmonized with the fire behaviour prediction model FireStation, the classes used in the fuel map are consistent with those used in FireStation. As can be seen in Figure 22, in the area of interest there are seven different fuel classes. Grassland is that one with higher representativeness.

Figure 22: Fuel map for the region of L’Aquila.

The fuel map showed in Figure 22 was developed, in April 2014, by the ArcFUEL Project Consortium from a cooperation protocol established between CRISMA Project and the European LIFE+ Project ArcFUEL.

3.2.2.3. Transition probability data for the cable failure triggered by an earthquake

The cables connected to low voltage distribution lines (evidenced in red in Figure 23) are those considered as the most vulnerable ones to external shocks (e.g. seismic excitation). Small poles have a high probability to fall down during or after an earthquake than larger poles. However, during a seismic event, all the poles (low, medium and high voltage) shake and the stress induced in the electric cables may cause its breakage.

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Figure 23: Sketch of a T&D system for an EPN (TL = Transmission Lines, D = Distribution lines, TD [HV MV] = Transformation (from high to medium voltage) and Distribution station, TD [MV LV] = Transformation (from medium to low voltage) and Distribution station, L = Load) (adapted from D5.2

Deliverable of the EU project SYNER-G).

In particular, the electricity poles used in electricity distribution lines may be subdivided based on the material (wood, reinforced concrete or tubular steel), diameter and height. The tubular steel electric poles are very common and for this reason we concentrated on this typology. In Figure 24 there is an example table classifying the electricity poles of this category according to the diameter and height.

Figure 24: An example of tubular steel poles classification (from a commercial producer).

In order to assess the probability of the cable failure, a number of pole classes are studied. In particular, referring to a generic class, the fragility curve represents the probability of attaining a limit value of displacement at the pole top varying the intensity of the seismic input. Such limit value is represented as a fraction of a displacement XMAX (three

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hypotheses are considered: 0.5 XMAX, XMAX and 1.25 XMAX, where XMAX is defined as the displacement corresponding to the static application of the design force T1 (see Figure 25)

Figure 25: Definition of top displacement XMAX.

The intensity input may be expressed with various measures. Here we adopt the peak ground acceleration.

In order to determine the probability of attaining xlim, an Incremental Dynamic Analysis (IDA) is performed (Vamvatsikos and Cornell, 2002). Figure 26 shows the normalized pseudo-acceleration spectra of the selected records, together with the mean spectrum and comparison with EC8 based representation.

Figure 26 – Normalized pseudo-acceleration spectra.

In order to perform the IDA, the pole is schematized as a Single Degree Of Freedom System (SDOF) with a concentrated mass on top (see Figure 27), suitably characterized by stiffness k and elastic period T; 5% critical damping is assumed.

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Figure 27: Representation of the electric poles and the mass on the top.

Figure 28 shows an example IDA obtained for the pole type 12B14.

Figure 28: IDA for the pole type 12B14.

Elaborating IDA results with the approach proposed in (Porter et al., 2007) the fragility curves can be obtained. Figure 29 show the fragility curves derived for pole type 12B14.

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Figure 29: Fragility curves of electric poles to a transversal force T1.

In order to simplify the use case, all the electricity poles of the area of interest will follow the fragility curve XMAX.

Transition probability data for fire ignition triggered by cable failure

The transition from the electric cable failure to a fire ignition has two evolutionary steps: 1) the production of the electric arc from the electricity cable to the fuel bed, and 2) the ignition of the fuel bed by the electric arc.

It is assumed that the failure of an electric cable imperatively causes an electric arc. Due to the existing protection systems the electric charge is normally interrupted in case of failure and after that, for brief instants, the residual electric charge continues flowing. After the cable breakage, the cable takes some time to land or to reach a sufficient distance to establish the electric arc. The time to ignition by electric arc with high voltage is very short (tenths of seconds) and therefore in this sense it is reasonable to consider the production of an electric arc for a short period as inevitable. On the other hand, the time to ionize the air path from the cable failure to the fuel bed and the consequent production of the electric arc is strongly dependent on the distance and on the resistance of this path. This dependency is a function of many additional factors such as topography, cable height, air humidity, and pressure. For simplification reasons we will not take those aspects into consideration in this document, but rather assume that the cable failure will always drive to an electric arc that reaches the fuel bed.

In a laboratory environment, several tests were carried out in order

to determine the total amount of energy required to ignite a certain

amount of fuel. These tests were performed for straw and pine

needles (Pinus pinaster) for a range of fuel moisture content (FMC)

between 9.2% and 12.2% (APPENDIX (2) Main parameters of the

laboratorial tests for the determination of ignition by industrial

electric discharge.

0 5 10 15 20 25 30 35 40 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Fragility 12B14

PGA(g) P [X > X L 3 ] 0.5 X MAX X MAX 1.25 X MAX

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). A bouquet of the fuel material was exposed to an industrial electrical arc of certain power (P [kVA]) as can be seen in Figure 30. The time (t [s]) elapsed from the beginning of discharge until the evidence of combustion was measured. The energy (E[kJ]) required for ignition was determined using Equation 4.

a) b) c) d)

Figure 30: Sequence of images of a laboratorial test to determine the energy required for ignition by industrial electric discharge.

t P

E [4]

It was found that the probability of ignition was not significantly different for the several tests at different conditions of FMC or different types of fuel (straw or pine needles) and also the fuel load does not seem really relevant for the probability of ignition. This statement should be considered preliminary since not many tests with fuels other than straw and pine needles were carried out and the range of fuel moisture content was not very extensive.

Using the data collected in the laboratory experiments, we estimated the parameter of different competing probabilistic models in order to find the distribution providing the best description of the laboratory observations. As possible competing models we used the Log-normal, Gamma, Normal, Weibull and Exponential distributions. We estimate the model parameters for each candidate model using a Maximum Likelihood Estimate (MLE) approach, and use the Akaike Information criteria (AIC, Akaike 1974) for the model selection. The AIC is a tool based on the concept of entropy, and offers a relative measure of the information lost when a given model is used to describe some data (a tradeoff between accuracy and complexity of the model).

Table 3 summarizes the functional form of the PDF, estimated (MLE) parameters (and uncertainties), and the AIC for all the probabilistic models considered. Using a Kolmogorov–Smirnov test, we cannot reject the Log-normal, Gamma, and Normal hypotheses (at significance level of 0.05), which means that, from a statistical point of view, all these probability models successfully explain the observed data. However, according with the AIC values, the preferred model (i.e. that with the lowest AIC value) is the Log-normal.

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Table 3: Candidate distributions, PDF, estimated (MLE) model parameters and uncertainties, and Akaike Information Criteria (AIC). According to the AIC information, the model that best describes

the data is the Lognormal.

Model Probability density Parameters

(MLE) AIC Log-normal ( , ) = ( | , ) = ( ) _{=6.334 [6.329,6.339]} =0.246 [0.243,0.250] – 62803 Gamma (a,b) = ( | , ) = ( ) a=16.68 [16.22,17.14] b=34.84 [33.87,35.83] –62845 Normal ( , ) = ( | , ) = ( ) _{=581.0 [578.1,583.8]} =146.3 [144.2,148.3] –63319 Weibull (a,b) = ( | , ) = a=637.1 [633.7,640.6] b=3.87 [3.81,3.92] –63832 Exponential ( ) = ( | ) = =581.0 [569.7,592.6] – 72815

Figure 31a shows the cumulative probability (CDF) of the candidate distributions and the empirical CDF of the observed data, while the Figure 31b shows the CDF and related uncertainties of the Log-normal model.

a) b)

Figure 31: a) Plot of the Cumulative Distribution Function (CDF) of Log-normal, Gamma, Normal, Weibull, and Exponential competing models, and the empirical CDF of the observed data; b) CDF of

the Log-normal (with parameters as those presented in Table 3), selected as the best model describing the observations.

Therefore, the model selected to describe the energy required to start an ignition is a Log-normal with parameters =6.334 [6.329,6.339] and =0.246 [0.243,0.250], where the values in parenthesis represent uncertainty bounds. The Log-normal model is therefore used for the determination of the probability of fire ignition given the occurrence of an electric discharge. As for simplicity we consider that an electric cable failure will always generate an electric discharge, this model consequently also represents the probability of a fire ignition given a cable failure.

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As previously mentioned, there was no significant difference in the results achieved for pine needles as compared to straw. Therefore, for simplification, we assume that the model can provide the probability of ignition for all fuel classes other than urban areas, where the probability of ignition by electric discharge is assumed to be zero. This assumption seems reasonable as commonly, for prevention reasons, the area below electric cables is cleaned of heavy fuels.

3.2.2.4.

With all the required inputs available, the cascade effect model is prepared to show the most interesting outputs to the end user. In this application there are two different use cases: (1) earthquake – cable failure, and (2) earthquake – cable failure – fire ignition. Earthquake – Cable failure

Figure 32 shows the distribution of probabilities to have a cable failure in the electric network after a seismic event with a Mw 5.6. Probability distribution results from the earthquake intensity distribution (Figure 21) and the fragility curve of the poles (Figure 29).

Figure 32: Map for probability of cable failure.

Obviously, only in the electric network is possible to have cable failure. As it was previously explained the cable failures which we are dealing with occur close to the electric poles. Therefore, the information on the cable failure probability is presented around the poles. In this case, a unique electric poles fragility curve was used (XMAX=1) and therefore the probability of cable failure depends exclusively on the distance between each pole and the seismic epicentre.

Earthquake – Cable failure – Fire ignition

Besides the visualization of the map for probability cable failure due to the earthquake, the user may want to access the probabilities of fire ignition due to cable failure. This information is provided in the form of a distribution map as showed in Figure 33. The map for distribution of fire ignition by electric cable failure is obtained from the fuel map (Figure

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22), the geographic location of the electric network (Figure 20) and the probability function of fire ignition (Equation 5).

As previously explained, the probability of fire ignition is very dependent on the energy released by the electric discharge. The value of energy released during the discharge was determined assuming a trigger time (electric interruption) of 0.1s, a power of 8000kVA (current of 53A and voltage value of 150kV) and consequently 800kJ of energy for the first 20km of electric line (first 67 poles on the right side of Figure 33). It was also assumed a withdrawal of 100kJ of energy due to energy consumption and losses for every 20km of electric line having as consequence a decrease of fire ignition probability along the electric network.

Figure 33: Map for probability of fire ignition due to an electric cable failure.

If the user intends directly evaluate the fire ignition probability triggered by electric cable failure due to a seismic event, it is necessary to apply a combined probability. In this use case, the probability of fire ignition is the product of the probability of cable failure due an earthquake of a certain seismic intensity distribution and the probability of having a fire ignition due to a cable failure (Equation 6). Figure 34 shows the probability map of fire ignition triggered by electric cable failure due to a seismic event.

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Figure 34: Map for probability of fire ignition by electric cable failure due to earthquake.

Supported by the information on the probability to have a fire ignition around each pole, the user may simulate an ignition in a certain location according to the most likely spot to have an ignition, the proximity of an important infrastructure or any other convenience. The simulation may be performed accessing FireStation fire behaviour simulation tool in order to simulate the spread of the fire and other important parameters (Figure 35). The comparison of impacts caused by both earthquake and forest fire in a cascade effect event may be an important information to support the decision making. The cascade effect tool is used only to assess the probability of occurrence of a predefined event chain, in the present case Earthquake damage to electricity network (cable failure) fire ignition. The use of FireStation and the analysis of impacts are out of the use of cascade effect tool but it is integrated in the general CRISMA tool where the user must return to perform this last simulation.

(a) (b) (c)

Figure 35 – Map of the evolution of the forest fire (a) after the ignition by the electric discharge from a probable ignition point and other possible outputs from the FireStation software: b) rate of spread, c)

Database and Model for Dynamic scenario assessment V2